DopeLearning: A computational approach to rap lyrics generation

Eric Malmi, Pyry Takala, Hannu Toivonen, Tapani Raiko, Aristides Gionis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

14 Citations (Scopus)

Abstract

Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the Rank SVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.

Original languageEnglish
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherACM
Pages195-204
Number of pages10
Volume13-17-August-2016
ISBN (Electronic)9781450342322
DOIs
Publication statusPublished - 13 Aug 2016
MoE publication typeA4 Article in a conference publication
EventACM SIGKDD International Conference on Knowledge Discovery and Data Mining - San Francisco, United States
Duration: 13 Aug 201617 Aug 2016
Conference number: 22

Conference

ConferenceACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Abbreviated titleKDD
CountryUnited States
CitySan Francisco
Period13/08/201617/08/2016

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